229 citations · 624 across the 50 of their papers we have counts for
54 papers
Graph Positional and Structural Encoder
Semih Cantürk, Renming Liu, Olivier Lapointe-Gagné +4
Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, rendering them essential tools for empowering modern GNNs, and in particular graph…
Simulation-free Schrödinger bridges via score and flow matching
Alexander Tong, Nikolay Malkin, Kilian Fatras +5
We present simulation-free score and flow matching ([SF]M), a simulation-free objective for inferring stochastic dynamics given unpaired samples drawn from arbitrary source and…
Inferring dynamic regulatory interaction graphs from time series data with perturbations
Dhananjay Bhaskar, Sumner Magruder, Edward De Brouwer +4
Complex systems are characterized by intricate interactions between entities that evolve dynamically over time. Accurate inference of these dynamic relationships is crucial for und…
Neural FIM for learning Fisher Information Metrics from point cloud data
Oluwadamilola Fasina, Guillaume Huguet, Alexander Tong +5
Although data diffusion embeddings are ubiquitous in unsupervised learning and have proven to be a viable technique for uncovering the underlying intrinsic geometry of data, diffus…
Graph Fourier MMD for Signals on Graphs
Samuel Leone, Aarthi Venkat, Guillaume Huguet +3
While numerous methods have been proposed for computing distances between probability distributions in Euclidean space, relatively little attention has been given to computing such…
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction
Guillaume Huguet, Alexander Tong, Edward De Brouwer +4
Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensional, high throughput, noisy datasets. Su…